期刊文献+

大数据及人工智能技术在创伤救治中应用的研究进展 被引量:1

Research progress in application of big data and artificial intelligence technology in trauma treatment
原文传递
导出
摘要 创伤具有病情复杂、致残率和致死率高等特点,救治难度大,目前创伤救治模式由于医疗条件限制、资源调度分配不及时等原因,仍然存在急救效率低与流程应用不规范等不足,创伤患者的救治面临巨大挑战。创伤患者在救治过程中会产生大量对病情诊断及治疗有价值的动态数据。大数据及人工智能技术是基于大规模的数据集合,对给定的数据进行合理预判或估算的算法,现已应用于创伤患者的救治模式中,其高效精准的大数据统计分析,以及机器学习、规划决策等创新医疗技术既提高了创伤患者救治的效率和安全性,同时也降低了临床医师的工作负荷,弥补了传统创伤救治模式的不足。笔者就大数据及人工智能技术在创伤患者院前急救和院内诊治过程中应用的研究进展进行综述,以期为创伤救治提供参考。 Trauma has the characteristics of complex disease,high disability rate and fatality rate,which adds difficulty to treatment.Due to the limitation of medical conditions and untimely allocation of resources,the current trauma treatment modes still have shortcomings such as low first aid efficiency and irregular application,and hence the treatment is facing enormous challenges.In the process of trauma treatment,a large amount of dynamic data that are valuable for disease diagnosis and treatment will be generated.Big data and artificial intelligence technology is an algorithm that can reasonably predict or estimate the given data based on large‑scale data collection,and has been applied to trauma treatment modes.The efficient and accurate statistical analysis of big data and innovative medical technology directions such as machine learning,planning and decision‑making not only improve the efficiency and safety of trauma treatment,but also reduce the workload of clinicians,making up for the shortcomings of traditional trauma treatment modes.The authors mainly review the application of big data and artificial intelligence technology in pre‑hospital first aid and in‑hospital diagnosis and treatment for trauma patients,in order to provide a reference for trauma treatment.
作者 步涨 代国洋 徐峰 Bu Zhang;Dai Guoyang;Xu Feng(Department of Emergency Trauma Surgery,First Affiliated Hospital of Soochow University,Suzhou 215006,China)
出处 《中华创伤杂志》 CAS CSCD 北大核心 2022年第10期955-960,共6页 Chinese Journal of Trauma
关键词 人工智能 创伤和损伤 急救 大数据 Artificial intelligence Wounds and injuries First aid Big data
  • 相关文献

参考文献7

二级参考文献36

共引文献71

同被引文献15

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部